In some examples, users may be interested in generating responses corresponding to content. For instance, teachers may be interested in generating responses that are questions that correspond to a piece of content to assess their students' reading comprehension, math skills, etc. Manually generating questions is time consuming and as such, requires tremendous amounts of human labor and money. Current automatic techniques for generating questions focus on the grammaticality of question generation to generate “wh-questions” from single sentences. Other automatic techniques create fill-in-the-blank questions from single sentences to ensure grammaticality. That is, current techniques automatically generate questions directed to specific facts (e.g., factoid questions) in a single sentence of text. As a result, fully automated computer generated questions often lack scope and depth.